1. Field of the Invention
The present invention relates to target recognition. More particularly, the present invention relates to automatic target recognition systems which employ a filter to pre-screen image data and nominate candidate targets.
2. Background Information
Weapon system performance, and in particular, military weapon system performance is generally measured in terms of system accuracy. In weapon delivery systems, accuracy may depend on the system's ability to deliver a weapon on target. In weapon systems that track targets, accuracy typically depends on the ability to periodically or continuously establish the correct position of a target. To improve accuracy, these systems often employ automatic target recognition systems and/or techniques.
Automatic target recognition generally involves processing large quantities of image data (e.g., thermal image data) in order to identify and locate candidate targets. Automatic target recognition system performance, in turn, is generally measured in terms of probability of detection (Pd) and probability of false detection (Pfd), where Pd represents the likelihood that the system correctly detected and classified a target as a true target, and where Pfd represents the likelihood that the system incorrectly classified a non-target as a true target. Curve A in FIG. 1 illustrates the relationship between Pd and Pfd for a typical automatic target recognition system. Clearly, it is more desirable to have a system which maximizes Pd while minimizing Pfd, as indicated by the dotted line to the left of curve A, as compared to the dashed line to the right of curve A.
Conventional target recognition systems employ what is known as pipeline processing to maximize performance. Pipeline processing is a well-known term which refers to a data processing technique that involves multiple, concurrently operating processing stages, where each stage performs a separate processing function.
FIG. 2 illustrates an exemplary pipeline processing sequence 200 which might be employed in a conventional target recognition system. As shown, the exemplary pipeline processing sequence includes a pre-screen filter 205, a target delineator 210, a feature extractor 215 and a target classifier 220. The pre-screen filter 205 processes the image data and, based on this processing, identifies candidate targets. Typically, the pre-screen filter 205 also provides a location of each candidate target identified within the image, e.g., by providing an x-y coordinate of a pixel that corresponds with the spatial center of each candidate target. The target delineator 210 then uses the image data and the location information to determine the extent or boundary of any candidate target. For instance, the target delineator 210 may determine which pixels in proximity to the spatial center of a given candidate target also reflect or correspond to a portion of the candidate target. The feature extractor 215, as the name suggests, measures certain predefined target features, such as length, width, perimeter and texture. The feature extractor 215 uses the measurements to generate a vector for the candidate target. The vector is then passed to the target classifier 220. The target classifier 220, which has been previously trained using measurements of features from known true targets and known false targets, classifies the candidate target as a true target or a non-true target based on the vector it received from the feature extractor 215.
The pre-screen filter stage 205 illustrated in FIG. 2 is of particular importance. As stated, it identifies and locates candidate targets. As such, it establishes the processing load for the remaining stages. If the filter nominates a large number of candidate targets, the processing load is relatively large. If the filter nominates a small number of candidate targets, the processing load is relatively small. Therefore, it is important to employ a filter that effectively nominates targets, that is, with a relatively high Pd and a relatively low Pfd. Else, a significant amount of time is wasted processing data associated with false targets. It is also important to employ a filter that efficiently processes image data so that candidate targets are nominated in a timely manner.